AI-Powered Newsletter Systems
How we built AI Insider's personalized content delivery system using OpenAI. Architecture decisions for subscription-based content platforms.
The AI Insider Vision
AI Insider aimed to solve information overload. Instead of users searching for AI news, we would curate and deliver personalized content based on their interests.
System Architecture
Content Pipeline
The Role of OpenAI
We used GPT for several tasks:
Content Summarization
def summarize_article(content: str) -> str:
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "Summarize this article in 2-3 sentences."},
{"role": "user", "content": content}
]
)
return response.choices[0].message.content
Personalized Introductions
Each newsletter had a personalized intro based on the user's reading history.
Content Recommendations
We combined collaborative filtering with GPT-based similarity scoring.
Subscription Model
Tiers
Implementation
class Subscription(models.Model):
user = models.OneToOneField(User, on_delete=models.CASCADE)
tier = models.CharField(max_length=20, choices=TIER_CHOICES)
features = models.JSONField(default=dict)
def can_access(self, feature: str) -> bool:
return feature in TIER_FEATURES[self.tier]
Personalization Engine
User Preferences
We tracked:
Content Matching
def score_content_for_user(user: User, content: Content) -> float:
explicit_score = match_topics(user.preferences, content.topics)
implicit_score = similarity_to_history(user.reading_history, content)
recency_score = calculate_recency(content.published_at)
return (
0.4 * explicit_score +
0.4 * implicit_score +
0.2 * recency_score
)
Results
After launch:
Challenges
Cost Management
OpenAI API calls add up. We implemented:
Quality Control
AI-generated content needs human oversight:
Key Takeaways
Building AI Insider taught me that the best AI products feel magical but are built on solid engineering foundations.